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Diffstat (limited to 'source/use_case/kws_asr/src/UseCaseHandler.cc')
| -rw-r--r-- | source/use_case/kws_asr/src/UseCaseHandler.cc | 707 |
1 files changed, 707 insertions, 0 deletions
diff --git a/source/use_case/kws_asr/src/UseCaseHandler.cc b/source/use_case/kws_asr/src/UseCaseHandler.cc new file mode 100644 index 0000000..c50796f --- /dev/null +++ b/source/use_case/kws_asr/src/UseCaseHandler.cc @@ -0,0 +1,707 @@ +/* + * Copyright (c) 2021 Arm Limited. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "UseCaseHandler.hpp" + +#include "hal.h" +#include "InputFiles.hpp" +#include "AudioUtils.hpp" +#include "UseCaseCommonUtils.hpp" +#include "DsCnnModel.hpp" +#include "DsCnnMfcc.hpp" +#include "Classifier.hpp" +#include "KwsResult.hpp" +#include "Wav2LetterMfcc.hpp" +#include "Wav2LetterPreprocess.hpp" +#include "Wav2LetterPostprocess.hpp" +#include "AsrResult.hpp" +#include "AsrClassifier.hpp" +#include "OutputDecode.hpp" + + +using KwsClassifier = arm::app::Classifier; + +namespace arm { +namespace app { + + enum AsrOutputReductionAxis { + AxisRow = 1, + AxisCol = 2 + }; + + struct KWSOutput { + bool executionSuccess = false; + const int16_t* asrAudioStart = nullptr; + int32_t asrAudioSamples = 0; + }; + + /** + * @brief Helper function to increment current audio clip index + * @param[in,out] ctx pointer to the application context object + **/ + static void _IncrementAppCtxClipIdx(ApplicationContext& ctx); + + /** + * @brief Helper function to increment current audio clip index + * @param[in,out] ctx pointer to the application context object + **/ + static void _IncrementAppCtxClipIdx(ApplicationContext& ctx); + + /** + * @brief Helper function to set the audio clip index + * @param[in,out] ctx pointer to the application context object + * @param[in] idx value to be set + * @return true if index is set, false otherwise + **/ + static bool _SetAppCtxClipIdx(ApplicationContext& ctx, uint32_t idx); + + /** + * @brief Presents kws inference results using the data presentation + * object. + * @param[in] platform reference to the hal platform object + * @param[in] results vector of classification results to be displayed + * @param[in] infTimeMs inference time in milliseconds, if available + * Otherwise, this can be passed in as 0. + * @return true if successful, false otherwise + **/ + static bool _PresentInferenceResult(hal_platform& platform, std::vector<arm::app::kws::KwsResult>& results); + + /** + * @brief Presents asr inference results using the data presentation + * object. + * @param[in] platform reference to the hal platform object + * @param[in] results vector of classification results to be displayed + * @param[in] infTimeMs inference time in milliseconds, if available + * Otherwise, this can be passed in as 0. + * @return true if successful, false otherwise + **/ + static bool _PresentInferenceResult(hal_platform& platform, std::vector<arm::app::asr::AsrResult>& results); + + /** + * @brief Returns a function to perform feature calculation and populates input tensor data with + * MFCC data. + * + * Input tensor data type check is performed to choose correct MFCC feature data type. + * If tensor has an integer data type then original features are quantised. + * + * Warning: mfcc calculator provided as input must have the same life scope as returned function. + * + * @param[in] mfcc MFCC feature calculator. + * @param[in,out] inputTensor Input tensor pointer to store calculated features. + * @param[in] cacheSize Size of the feture vectors cache (number of feature vectors). + * + * @return function function to be called providing audio sample and sliding window index. + **/ + static std::function<void (std::vector<int16_t>&, int, bool, size_t)> + GetFeatureCalculator(audio::DsCnnMFCC& mfcc, + TfLiteTensor* inputTensor, + size_t cacheSize); + + /** + * @brief Performs the KWS pipeline. + * @param[in,out] ctx pointer to the application context object + * + * @return KWSOutput struct containing pointer to audio data where ASR should begin + * and how much data to process. + */ + static KWSOutput doKws(ApplicationContext& ctx) { + constexpr uint32_t dataPsnTxtInfStartX = 20; + constexpr uint32_t dataPsnTxtInfStartY = 40; + + constexpr int minTensorDims = static_cast<int>( + (arm::app::DsCnnModel::ms_inputRowsIdx > arm::app::DsCnnModel::ms_inputColsIdx)? + arm::app::DsCnnModel::ms_inputRowsIdx : arm::app::DsCnnModel::ms_inputColsIdx); + + KWSOutput output; + + auto& kwsModel = ctx.Get<Model&>("kwsmodel"); + if (!kwsModel.IsInited()) { + printf_err("KWS model has not been initialised\n"); + return output; + } + + const int kwsFrameLength = ctx.Get<int>("kwsframeLength"); + const int kwsFrameStride = ctx.Get<int>("kwsframeStride"); + const float kwsScoreThreshold = ctx.Get<float>("kwsscoreThreshold"); + + TfLiteTensor* kwsOutputTensor = kwsModel.GetOutputTensor(0); + TfLiteTensor* kwsInputTensor = kwsModel.GetInputTensor(0); + + if (!kwsInputTensor->dims) { + printf_err("Invalid input tensor dims\n"); + return output; + } else if (kwsInputTensor->dims->size < minTensorDims) { + printf_err("Input tensor dimension should be >= %d\n", minTensorDims); + return output; + } + + const uint32_t kwsNumMfccFeats = ctx.Get<uint32_t>("kwsNumMfcc"); + const uint32_t kwsNumAudioWindows = ctx.Get<uint32_t>("kwsNumAudioWins"); + + audio::DsCnnMFCC kwsMfcc = audio::DsCnnMFCC(kwsNumMfccFeats, kwsFrameLength); + kwsMfcc.Init(); + + /* Deduce the data length required for 1 KWS inference from the network parameters. */ + auto kwsAudioDataWindowSize = kwsNumAudioWindows * kwsFrameStride + + (kwsFrameLength - kwsFrameStride); + auto kwsMfccWindowSize = kwsFrameLength; + auto kwsMfccWindowStride = kwsFrameStride; + + /* We are choosing to move by half the window size => for a 1 second window size, + * this means an overlap of 0.5 seconds. */ + auto kwsAudioDataStride = kwsAudioDataWindowSize / 2; + + info("KWS audio data window size %u\n", kwsAudioDataWindowSize); + + /* Stride must be multiple of mfcc features window stride to re-use features. */ + if (0 != kwsAudioDataStride % kwsMfccWindowStride) { + kwsAudioDataStride -= kwsAudioDataStride % kwsMfccWindowStride; + } + + auto kwsMfccVectorsInAudioStride = kwsAudioDataStride/kwsMfccWindowStride; + + /* We expect to be sampling 1 second worth of data at a time + * NOTE: This is only used for time stamp calculation. */ + const float kwsAudioParamsSecondsPerSample = 1.0/audio::DsCnnMFCC::ms_defaultSamplingFreq; + + auto currentIndex = ctx.Get<uint32_t>("clipIndex"); + + /* Creating a mfcc features sliding window for the data required for 1 inference. */ + auto kwsAudioMFCCWindowSlider = audio::SlidingWindow<const int16_t>( + get_audio_array(currentIndex), + kwsAudioDataWindowSize, kwsMfccWindowSize, + kwsMfccWindowStride); + + /* Creating a sliding window through the whole audio clip. */ + auto audioDataSlider = audio::SlidingWindow<const int16_t>( + get_audio_array(currentIndex), + get_audio_array_size(currentIndex), + kwsAudioDataWindowSize, kwsAudioDataStride); + + /* Calculate number of the feature vectors in the window overlap region. + * These feature vectors will be reused.*/ + size_t numberOfReusedFeatureVectors = kwsAudioMFCCWindowSlider.TotalStrides() + 1 + - kwsMfccVectorsInAudioStride; + + auto kwsMfccFeatureCalc = GetFeatureCalculator(kwsMfcc, kwsInputTensor, + numberOfReusedFeatureVectors); + + if (!kwsMfccFeatureCalc){ + return output; + } + + /* Container for KWS results. */ + std::vector<arm::app::kws::KwsResult> kwsResults; + + /* Display message on the LCD - inference running. */ + auto& platform = ctx.Get<hal_platform&>("platform"); + std::string str_inf{"Running KWS inference... "}; + platform.data_psn->present_data_text( + str_inf.c_str(), str_inf.size(), + dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0); + + info("Running KWS inference on audio clip %u => %s\n", + currentIndex, get_filename(currentIndex)); + + /* Start sliding through audio clip. */ + while (audioDataSlider.HasNext()) { + const int16_t* inferenceWindow = audioDataSlider.Next(); + + /* We moved to the next window - set the features sliding to the new address. */ + kwsAudioMFCCWindowSlider.Reset(inferenceWindow); + + /* The first window does not have cache ready. */ + bool useCache = audioDataSlider.Index() > 0 && numberOfReusedFeatureVectors > 0; + + /* Start calculating features inside one audio sliding window. */ + while (kwsAudioMFCCWindowSlider.HasNext()) { + const int16_t* kwsMfccWindow = kwsAudioMFCCWindowSlider.Next(); + std::vector<int16_t> kwsMfccAudioData = + std::vector<int16_t>(kwsMfccWindow, kwsMfccWindow + kwsMfccWindowSize); + + /* Compute features for this window and write them to input tensor. */ + kwsMfccFeatureCalc(kwsMfccAudioData, + kwsAudioMFCCWindowSlider.Index(), + useCache, + kwsMfccVectorsInAudioStride); + } + + info("Inference %zu/%zu\n", audioDataSlider.Index() + 1, + audioDataSlider.TotalStrides() + 1); + + /* Run inference over this audio clip sliding window. */ + arm::app::RunInference(platform, kwsModel); + + std::vector<ClassificationResult> kwsClassificationResult; + auto& kwsClassifier = ctx.Get<KwsClassifier&>("kwsclassifier"); + + kwsClassifier.GetClassificationResults( + kwsOutputTensor, kwsClassificationResult, + ctx.Get<std::vector<std::string>&>("kwslabels"), 1); + + kwsResults.emplace_back( + kws::KwsResult( + kwsClassificationResult, + audioDataSlider.Index() * kwsAudioParamsSecondsPerSample * kwsAudioDataStride, + audioDataSlider.Index(), kwsScoreThreshold) + ); + + /* Keyword detected. */ + if (kwsClassificationResult[0].m_labelIdx == ctx.Get<uint32_t>("keywordindex")) { + output.asrAudioStart = inferenceWindow + kwsAudioDataWindowSize; + output.asrAudioSamples = get_audio_array_size(currentIndex) - + (audioDataSlider.NextWindowStartIndex() - + kwsAudioDataStride + kwsAudioDataWindowSize); + break; + } + +#if VERIFY_TEST_OUTPUT + arm::app::DumpTensor(kwsOutputTensor); +#endif /* VERIFY_TEST_OUTPUT */ + + } /* while (audioDataSlider.HasNext()) */ + + /* Erase. */ + str_inf = std::string(str_inf.size(), ' '); + platform.data_psn->present_data_text( + str_inf.c_str(), str_inf.size(), + dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0); + + if (!_PresentInferenceResult(platform, kwsResults)) { + return output; + } + + output.executionSuccess = true; + return output; + } + + /** + * @brief Performs the ASR pipeline. + * + * @param ctx[in/out] pointer to the application context object + * @param kwsOutput[in] struct containing pointer to audio data where ASR should begin + * and how much data to process + * @return bool true if pipeline executed without failure + */ + static bool doAsr(ApplicationContext& ctx, const KWSOutput& kwsOutput) { + constexpr uint32_t dataPsnTxtInfStartX = 20; + constexpr uint32_t dataPsnTxtInfStartY = 40; + + auto& platform = ctx.Get<hal_platform&>("platform"); + platform.data_psn->clear(COLOR_BLACK); + + /* Get model reference. */ + auto& asrModel = ctx.Get<Model&>("asrmodel"); + if (!asrModel.IsInited()) { + printf_err("ASR model has not been initialised\n"); + return false; + } + + /* Get score threshold to be applied for the classifier (post-inference). */ + auto asrScoreThreshold = ctx.Get<float>("asrscoreThreshold"); + + /* Dimensions of the tensor should have been verified by the callee. */ + TfLiteTensor* asrInputTensor = asrModel.GetInputTensor(0); + TfLiteTensor* asrOutputTensor = asrModel.GetOutputTensor(0); + const uint32_t asrInputRows = asrInputTensor->dims->data[arm::app::Wav2LetterModel::ms_inputRowsIdx]; + + /* Populate ASR MFCC related parameters. */ + auto asrMfccParamsWinLen = ctx.Get<uint32_t>("asrframeLength"); + auto asrMfccParamsWinStride = ctx.Get<uint32_t>("asrframeStride"); + + /* Populate ASR inference context and inner lengths for input. */ + auto asrInputCtxLen = ctx.Get<uint32_t>("ctxLen"); + const uint32_t asrInputInnerLen = asrInputRows - (2 * asrInputCtxLen); + + /* Make sure the input tensor supports the above context and inner lengths. */ + if (asrInputRows <= 2 * asrInputCtxLen || asrInputRows <= asrInputInnerLen) { + printf_err("ASR input rows not compatible with ctx length %u\n", asrInputCtxLen); + return false; + } + + /* Audio data stride corresponds to inputInnerLen feature vectors. */ + const uint32_t asrAudioParamsWinLen = (asrInputRows - 1) * + asrMfccParamsWinStride + (asrMfccParamsWinLen); + const uint32_t asrAudioParamsWinStride = asrInputInnerLen * asrMfccParamsWinStride; + const float asrAudioParamsSecondsPerSample = + (1.0/audio::Wav2LetterMFCC::ms_defaultSamplingFreq); + + /* Get pre/post-processing objects */ + auto& asrPrep = ctx.Get<audio::asr::Preprocess&>("preprocess"); + auto& asrPostp = ctx.Get<audio::asr::Postprocess&>("postprocess"); + + /* Set default reduction axis for post-processing. */ + const uint32_t reductionAxis = arm::app::Wav2LetterModel::ms_outputRowsIdx; + + /* Get the remaining audio buffer and respective size from KWS results. */ + const int16_t* audioArr = kwsOutput.asrAudioStart; + const uint32_t audioArrSize = kwsOutput.asrAudioSamples; + + /* Audio clip must have enough samples to produce 1 MFCC feature. */ + std::vector<int16_t> audioBuffer = std::vector<int16_t>(audioArr, audioArr + audioArrSize); + if (audioArrSize < asrMfccParamsWinLen) { + printf_err("Not enough audio samples, minimum needed is %u\n", asrMfccParamsWinLen); + return false; + } + + /* Initialise an audio slider. */ + auto audioDataSlider = audio::ASRSlidingWindow<const int16_t>( + audioBuffer.data(), + audioBuffer.size(), + asrAudioParamsWinLen, + asrAudioParamsWinStride); + + /* Declare a container for results. */ + std::vector<arm::app::asr::AsrResult> asrResults; + + /* Display message on the LCD - inference running. */ + std::string str_inf{"Running ASR inference... "}; + platform.data_psn->present_data_text( + str_inf.c_str(), str_inf.size(), + dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0); + + size_t asrInferenceWindowLen = asrAudioParamsWinLen; + + /* Start sliding through audio clip. */ + while (audioDataSlider.HasNext()) { + + /* If not enough audio see how much can be sent for processing. */ + size_t nextStartIndex = audioDataSlider.NextWindowStartIndex(); + if (nextStartIndex + asrAudioParamsWinLen > audioBuffer.size()) { + asrInferenceWindowLen = audioBuffer.size() - nextStartIndex; + } + + const int16_t* asrInferenceWindow = audioDataSlider.Next(); + + info("Inference %zu/%zu\n", audioDataSlider.Index() + 1, + static_cast<size_t>(ceilf(audioDataSlider.FractionalTotalStrides() + 1))); + + Profiler prepProfiler{&platform, "pre-processing"}; + prepProfiler.StartProfiling(); + + /* Calculate MFCCs, deltas and populate the input tensor. */ + asrPrep.Invoke(asrInferenceWindow, asrInferenceWindowLen, asrInputTensor); + + prepProfiler.StopProfiling(); + std::string prepProfileResults = prepProfiler.GetResultsAndReset(); + info("%s\n", prepProfileResults.c_str()); + + /* Run inference over this audio clip sliding window. */ + arm::app::RunInference(platform, asrModel); + + /* Post-process. */ + asrPostp.Invoke(asrOutputTensor, reductionAxis, !audioDataSlider.HasNext()); + + /* Get results. */ + std::vector<ClassificationResult> asrClassificationResult; + auto& asrClassifier = ctx.Get<AsrClassifier&>("asrclassifier"); + asrClassifier.GetClassificationResults( + asrOutputTensor, asrClassificationResult, + ctx.Get<std::vector<std::string>&>("asrlabels"), 1); + + asrResults.emplace_back(asr::AsrResult(asrClassificationResult, + (audioDataSlider.Index() * + asrAudioParamsSecondsPerSample * + asrAudioParamsWinStride), + audioDataSlider.Index(), asrScoreThreshold)); + +#if VERIFY_TEST_OUTPUT + arm::app::DumpTensor(asrOutputTensor, asrOutputTensor->dims->data[arm::app::Wav2LetterModel::ms_outputColsIdx]); +#endif /* VERIFY_TEST_OUTPUT */ + + /* Erase */ + str_inf = std::string(str_inf.size(), ' '); + platform.data_psn->present_data_text( + str_inf.c_str(), str_inf.size(), + dataPsnTxtInfStartX, dataPsnTxtInfStartY, false); + } + if (!_PresentInferenceResult(platform, asrResults)) { + return false; + } + + return true; + } + + /* Audio inference classification handler. */ + bool ClassifyAudioHandler(ApplicationContext& ctx, uint32_t clipIndex, bool runAll) + { + auto& platform = ctx.Get<hal_platform&>("platform"); + platform.data_psn->clear(COLOR_BLACK); + + /* If the request has a valid size, set the audio index. */ + if (clipIndex < NUMBER_OF_FILES) { + if (!_SetAppCtxClipIdx(ctx, clipIndex)) { + return false; + } + } + + auto startClipIdx = ctx.Get<uint32_t>("clipIndex"); + + do { + KWSOutput kwsOutput = doKws(ctx); + if (!kwsOutput.executionSuccess) { + return false; + } + + if (kwsOutput.asrAudioStart != nullptr && kwsOutput.asrAudioSamples > 0) { + info("Keyword spotted\n"); + if(!doAsr(ctx, kwsOutput)) { + printf_err("ASR failed"); + return false; + } + } + + _IncrementAppCtxClipIdx(ctx); + + } while (runAll && ctx.Get<uint32_t>("clipIndex") != startClipIdx); + + return true; + } + + static void _IncrementAppCtxClipIdx(ApplicationContext& ctx) + { + auto curAudioIdx = ctx.Get<uint32_t>("clipIndex"); + + if (curAudioIdx + 1 >= NUMBER_OF_FILES) { + ctx.Set<uint32_t>("clipIndex", 0); + return; + } + ++curAudioIdx; + ctx.Set<uint32_t>("clipIndex", curAudioIdx); + } + + static bool _SetAppCtxClipIdx(ApplicationContext& ctx, const uint32_t idx) + { + if (idx >= NUMBER_OF_FILES) { + printf_err("Invalid idx %u (expected less than %u)\n", + idx, NUMBER_OF_FILES); + return false; + } + ctx.Set<uint32_t>("clipIndex", idx); + return true; + } + + static bool _PresentInferenceResult(hal_platform& platform, + std::vector<arm::app::kws::KwsResult>& results) + { + constexpr uint32_t dataPsnTxtStartX1 = 20; + constexpr uint32_t dataPsnTxtStartY1 = 30; + constexpr uint32_t dataPsnTxtYIncr = 16; /* Row index increment. */ + + platform.data_psn->set_text_color(COLOR_GREEN); + + /* Display each result. */ + uint32_t rowIdx1 = dataPsnTxtStartY1 + 2 * dataPsnTxtYIncr; + + for (uint32_t i = 0; i < results.size(); ++i) { + + std::string topKeyword{"<none>"}; + float score = 0.f; + + if (results[i].m_resultVec.size()) { + topKeyword = results[i].m_resultVec[0].m_label; + score = results[i].m_resultVec[0].m_normalisedVal; + } + + std::string resultStr = + std::string{"@"} + std::to_string(results[i].m_timeStamp) + + std::string{"s: "} + topKeyword + std::string{" ("} + + std::to_string(static_cast<int>(score * 100)) + std::string{"%)"}; + + platform.data_psn->present_data_text( + resultStr.c_str(), resultStr.size(), + dataPsnTxtStartX1, rowIdx1, 0); + rowIdx1 += dataPsnTxtYIncr; + + info("For timestamp: %f (inference #: %u); threshold: %f\n", + results[i].m_timeStamp, results[i].m_inferenceNumber, + results[i].m_threshold); + for (uint32_t j = 0; j < results[i].m_resultVec.size(); ++j) { + info("\t\tlabel @ %u: %s, score: %f\n", j, + results[i].m_resultVec[j].m_label.c_str(), + results[i].m_resultVec[j].m_normalisedVal); + } + } + + return true; + } + + static bool _PresentInferenceResult(hal_platform& platform, std::vector<arm::app::asr::AsrResult>& results) + { + constexpr uint32_t dataPsnTxtStartX1 = 20; + constexpr uint32_t dataPsnTxtStartY1 = 80; + constexpr bool allow_multiple_lines = true; + + platform.data_psn->set_text_color(COLOR_GREEN); + + /* Results from multiple inferences should be combined before processing. */ + std::vector<arm::app::ClassificationResult> combinedResults; + for (auto& result : results) { + combinedResults.insert(combinedResults.end(), + result.m_resultVec.begin(), + result.m_resultVec.end()); + } + + for (auto& result : results) { + /* Get the final result string using the decoder. */ + std::string infResultStr = audio::asr::DecodeOutput(result.m_resultVec); + + info("Result for inf %u: %s\n", result.m_inferenceNumber, + infResultStr.c_str()); + } + + std::string finalResultStr = audio::asr::DecodeOutput(combinedResults); + + platform.data_psn->present_data_text( + finalResultStr.c_str(), finalResultStr.size(), + dataPsnTxtStartX1, dataPsnTxtStartY1, allow_multiple_lines); + + info("Final result: %s\n", finalResultStr.c_str()); + return true; + } + + /** + * @brief Generic feature calculator factory. + * + * Returns lambda function to compute features using features cache. + * Real features math is done by a lambda function provided as a parameter. + * Features are written to input tensor memory. + * + * @tparam T feature vector type. + * @param inputTensor model input tensor pointer. + * @param cacheSize number of feature vectors to cache. Defined by the sliding window overlap. + * @param compute features calculator function. + * @return lambda function to compute features. + **/ + template<class T> + std::function<void (std::vector<int16_t>&, size_t, bool, size_t)> + _FeatureCalc(TfLiteTensor* inputTensor, size_t cacheSize, + std::function<std::vector<T> (std::vector<int16_t>& )> compute) + { + /* Feature cache to be captured by lambda function. */ + static std::vector<std::vector<T>> featureCache = std::vector<std::vector<T>>(cacheSize); + + return [=](std::vector<int16_t>& audioDataWindow, + size_t index, + bool useCache, + size_t featuresOverlapIndex) + { + T* tensorData = tflite::GetTensorData<T>(inputTensor); + std::vector<T> features; + + /* Reuse features from cache if cache is ready and sliding windows overlap. + * Overlap is in the beginning of sliding window with a size of a feature cache. + */ + if (useCache && index < featureCache.size()) { + features = std::move(featureCache[index]); + } else { + features = std::move(compute(audioDataWindow)); + } + auto size = features.size(); + auto sizeBytes = sizeof(T) * size; + std::memcpy(tensorData + (index * size), features.data(), sizeBytes); + + /* Start renewing cache as soon iteration goes out of the windows overlap. */ + if (index >= featuresOverlapIndex) { + featureCache[index - featuresOverlapIndex] = std::move(features); + } + }; + } + + template std::function<void (std::vector<int16_t>&, size_t , bool, size_t)> + _FeatureCalc<int8_t>(TfLiteTensor* inputTensor, + size_t cacheSize, + std::function<std::vector<int8_t> (std::vector<int16_t>& )> compute); + + template std::function<void (std::vector<int16_t>&, size_t , bool, size_t)> + _FeatureCalc<uint8_t>(TfLiteTensor* inputTensor, + size_t cacheSize, + std::function<std::vector<uint8_t> (std::vector<int16_t>& )> compute); + + template std::function<void (std::vector<int16_t>&, size_t , bool, size_t)> + _FeatureCalc<int16_t>(TfLiteTensor* inputTensor, + size_t cacheSize, + std::function<std::vector<int16_t> (std::vector<int16_t>& )> compute); + + template std::function<void(std::vector<int16_t>&, size_t, bool, size_t)> + _FeatureCalc<float>(TfLiteTensor* inputTensor, + size_t cacheSize, + std::function<std::vector<float>(std::vector<int16_t>&)> compute); + + + static std::function<void (std::vector<int16_t>&, int, bool, size_t)> + GetFeatureCalculator(audio::DsCnnMFCC& mfcc, TfLiteTensor* inputTensor, size_t cacheSize) + { + std::function<void (std::vector<int16_t>&, size_t, bool, size_t)> mfccFeatureCalc; + + TfLiteQuantization quant = inputTensor->quantization; + + if (kTfLiteAffineQuantization == quant.type) { + + auto* quantParams = (TfLiteAffineQuantization*) quant.params; + const float quantScale = quantParams->scale->data[0]; + const int quantOffset = quantParams->zero_point->data[0]; + + switch (inputTensor->type) { + case kTfLiteInt8: { + mfccFeatureCalc = _FeatureCalc<int8_t>(inputTensor, + cacheSize, + [=, &mfcc](std::vector<int16_t>& audioDataWindow) { + return mfcc.MfccComputeQuant<int8_t>(audioDataWindow, + quantScale, + quantOffset); + } + ); + break; + } + case kTfLiteUInt8: { + mfccFeatureCalc = _FeatureCalc<uint8_t>(inputTensor, + cacheSize, + [=, &mfcc](std::vector<int16_t>& audioDataWindow) { + return mfcc.MfccComputeQuant<uint8_t>(audioDataWindow, + quantScale, + quantOffset); + } + ); + break; + } + case kTfLiteInt16: { + mfccFeatureCalc = _FeatureCalc<int16_t>(inputTensor, + cacheSize, + [=, &mfcc](std::vector<int16_t>& audioDataWindow) { + return mfcc.MfccComputeQuant<int16_t>(audioDataWindow, + quantScale, + quantOffset); + } + ); + break; + } + default: + printf_err("Tensor type %s not supported\n", TfLiteTypeGetName(inputTensor->type)); + } + + + } else { + mfccFeatureCalc = mfccFeatureCalc = _FeatureCalc<float>(inputTensor, + cacheSize, + [&mfcc](std::vector<int16_t>& audioDataWindow) { + return mfcc.MfccCompute(audioDataWindow); + }); + } + return mfccFeatureCalc; + } +} /* namespace app */ +} /* namespace arm */
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